A. Mereu, Italy
Presenter of 5 Presentations
EPP0923 - Big Five personality traits prediction with AI
ABSTRACT
Introduction
Openness, conscientiousness, extroversion, agreeableness and neuroticism are known as the Big Five personality traits (BFPT). They are theoretical building blocks of the personality and comprise wide and interconnected spectra. Artificial intelligence (AI) could help to grasp their complexity.
Objectives
To investigate whether AI could predict the BFPT from themselves.
Methods
Data from 2,697 questionnaires were analysed using an AI. The short form of the International Personality Item Pool was used to assess the BFPT. Four of the BFPT scores were employed to predict the fifth one and the procedure was repeated for all of them alternatively. The AI was conservatively tuned to maximize the one-way random intraclass correlation coefficient (ICC) between predicted and real values. Their Pearson’s r was calculated too. The free and open source programming language R was used for all the analyses. Dataset source: Hansson, Isabelle; Berg, Anne Ingeborg; Thorvaldsson, Valgeir (2018), “Can personality predict longitudinal study attrition? Evidence from a population-based sample of older adults”, Mendeley Data, V1, doi: 10.17632/g3jx8zt2t9.1
Results
Openness, conscientiousness, extroversion, agreeableness and neuroticism predictions obtained ICC of 0.219, 0.146, 0.306, 0.354, 0.121 and Pearson’s r of 0.254, 0.149, 0.393, 0.446, 0.122 respectively. The results for extroversion and agreeableness were indicative of fair performance.
Conclusions
AI might be useful to predict personality traits, mainly extroversion and agreeableness. This could be utile in many situations, such as dealing with missing data or deciding whether to formally test someone. Finally, the AI used in this study is freely available, allowing anyone to experiment.
EPP1075 - SSRI-treated psychiatric disorders prediction with AI
ABSTRACT
Introduction
SSRI-treated psychiatric disorders (STPD), such as general anxiety disorder and major depression disorder, are common psychiatric diagnoses. Serotonin-mediated effects of solar insolation are an active topic of research. Artificial intelligence (AI) could help to better examine that complex relation.
Objectives
To investigate whether AI could predict the STPD relying primarily on average ambient temperature and annual solar insolation.
Methods
Data of age, average ambient temperature and annual solar insolation were employed to predict STPD status in 7,587 subjects using an AI. To simplify the data analysis, only individuals with white ethnicity were assessed. SPTD prevalence was 17.1%. The AI was conservatively tuned to maximize the positive likelihood ratio considering predicted and real STPD statuses. The free and open source programming language R was used for all the analyses. Dataset source: Wortzel, Joshua; Kent, Shia; Avery, David; Al-Hamdan, Mohammad; Turner, Brandon; Norden, Justin; Norden, Michael; Haynor, David (2018), “Data for: Ambient temperature and solar insolation are associated with decreased prevalence of SSRI-treated psychiatric disorders”, Mendeley Data, V1, doi: 10.17632/trs43ybh92.1
Results
Predictions obtained a positive likelihood ratio of 4.850. The results were indicative of fair performance.
Conclusions
AI might be useful to predict STPD. Furthermore, the results of this study might indicate a moderate effect of age, average ambient temperature and annual solar insolation on the probability of STPD occurrence. Finally, the AI used in this study is freely available, allowing anyone to experiment.
EPP1470 - Female sexual dysfunction after breast cancer surgery prediction with AI
ABSTRACT
Introduction
Female sexual dysfunction (FSD) can be overlooked. Different types of breast cancer surgery could have a different impact on the sexuality of women. Artificial intelligence (AI) could help to determine the relation between those conditions.
Objectives
To investigate whether AI could predict FSD relying primarily on the time elapsed after treatment and the type of breast cancer surgery.
Methods
Data of age, time elapsed after treatment and type of surgery (breast-conserving therapy and mastectomy) were employed to predict FSD status in 128 subjects using an AI. Women with and without steady relations were included in the analysis. FSD prevalence was 27.3%. The AI was conservatively tuned to maximize the positive likelihood ratio considering predicted and real FSD statuses. The free and open source programming language R was used for all the analyses. Dataset source: Nowosielski, Krzysztof; Krzystanek, Marek; Kowalczyk, Robert; Streb, Joanna; Kucharz, Jakub; Głogowska, Iwona; Lew-Starowicz, Zbigniew; Cedrych, Ida (2018), “Data for: Factors affecting sexual function and body image of early stage breast cancer survivors in Poland: A short-term observation.”, Mendeley Data, V1, doi: 10.17632/948n98trm6.1
Results
Predictions obtained a positive likelihood ratio of 5.314. The results were indicative of fair performance.
Conclusions
AI might be useful to predict FSD in women who undergo breast cancer surgery. Furthermore, the results of this study might indicate a moderate effect of age, time after treatment and type of surgery on the probability of FSD occurrence. Finally, the AI used in this study is freely available, allowing anyone to experiment.
O203 - Study retention prediction with AI
ABSTRACT
Introduction
Openness, conscientiousness, extroversion, agreeableness and neuroticism are dimensional personality traits known as the Big Five. Study attrition is a common but often hard to anticipate problem. Artificial intelligence (AI) could examine both fronts to mitigate the unpredictability of the latter.
Objectives
To investigate whether AI could predict study attrition employing personality traits scores.
Methods
Data from 2,697 questionnaires were analysed using an AI. The short form of the International Personality Item Pool was used to assess the Big Five personality traits on the first of three planned waves. The personality traits scores were employed to predict the missing of at least one wave. Overall attrition was 17.6%. The AI was conservatively tuned to minimize the negative likelihood ratio when confronting predicted and real attrition. The free and open source programming language R was used for all the analyses. Dataset source: Hansson, Isabelle; Berg, Anne Ingeborg; Thorvaldsson, Valgeir (2018), “Can personality predict longitudinal study attrition? Evidence from a population-based sample of older adults”, Mendeley Data, V1, doi: 10.17632/g3jx8zt2t9.1
Results
Predictions obtained a negative likelihood ratio of 0.333 and a negative predictive value of 0.933. The results were indicative of fair performance.
Conclusions
AI might be useful to predict study retention. Furthermore, the results of this study might indicate a moderate effect of the Big Five on the probability of study retention. Finally, the AI used in this study is freely available, allowing anyone to experiment.
O204 - Dark triad personality traits prediction with AI
ABSTRACT
Introduction
The dark triad is composed by the personality traits Machiavellianism, narcissism and psychopathy (MNP). Their complexity can make them difficult to interrelate. Artificial intelligence (AI) could help in this endeavour.
Objectives
To investigate whether AI could predict MNP from themselves.
Methods
Data from 210 questionnaires were analysed using an AI. The short Dark Triad questionnaire (SD3) was used to assess MNP. Two of the MNP scores were employed to predict the third one and the procedure was repeated for all of them alternatively. The AI was conservatively tuned to maximize the one-way random intraclass correlation coefficient (ICC) between predicted and real values. Pearson’s r was calculated too. The free and open source programming language R was used for all the analyses. Dataset source: Borráz-León, Javier I. (2020), “Dark triad, attractiveness, mate value, and sexual partners”, Mendeley Data, V1, doi: 10.17632/87vx6jfnrp.1
Results
Machiavellianism, narcissism and psychopathy predictions obtained ICC of 0.593, 0.335, 0.505 and Pearson’s r of 0.608, 0.346, 0.548 respectively. The results were indicative of fair performance, mainly for Machiavellianism and psychopathy.
Conclusions
AI might be useful to predict MNP. This could be utile in many situations, such as dealing with missing data or deciding whether to formally test someone. Finally, the AI used in this study is freely available, allowing anyone to experiment.